Ontology Learning Challenge Datasets

该数据集用于评估大型语言模型在本体学习挑战中的系统性能，特别关注在少量样本和零样本测试阶段的性能表现。这些数据集包含了真实标签，并使用标准的精确度、召回率和F1分数等评价指标进行评估。该任务的主题是本体学习。

This dataset is designed to assess the systematic performance of large language models (LLMs) within the ontology learning challenge, with particular emphasis on their performance during few-shot and zero-shot testing stages. It includes ground-truth labels, and its evaluation is conducted using standard metrics such as precision, recall, and F1-score. The task focuses on ontology learning.